The quality of input data directly impacts the performance of any machine learning model. This chapter focuses on the essential steps of preparing your data for analysis and model training using Julia. You will learn to load data from various sources into DataFrames.jl, a key Julia package for tabular data. We will cover techniques for cleaning data, such as addressing missing entries and identifying outliers. Additionally, you will be introduced to data transformation methods, including scaling numerical features, encoding categorical variables, and binning continuous data. We will also present principles of feature engineering and demonstrate how to create new, informative features from your existing data within the Julia environment. Finally, you will see how to use Julia's plotting libraries like Plots.jl and Makie.jl to visualize data, aiding in understanding and preprocessing. Upon completing this chapter, you will have the practical skills to manipulate and prepare datasets for machine learning tasks in Julia.